\section{Class List}
Here are the classes, structs, unions and interfaces with brief descriptions\+:\begin{DoxyCompactList}
\item\contentsline{section}{\hyperlink{classpgpr__chol}{pgpr\+\_\+chol} \\*This class provides Cholesky factorization and some related useful functions such as inverse, log-\/determinant etc }{\pageref{classpgpr__chol}}{}
\item\contentsline{section}{\hyperlink{classpgpr__cluster}{pgpr\+\_\+cluster} \\*Provides some basic clustering algorithms used in the approximated G\+P algorithms }{\pageref{classpgpr__cluster}}{}
\item\contentsline{section}{\hyperlink{classpgpr__cov}{pgpr\+\_\+cov} \\*Informaiton of covariance }{\pageref{classpgpr__cov}}{}
\item\contentsline{section}{\hyperlink{classpgpr__data}{pgpr\+\_\+data} \\*This class provides functionalities of preparing train data, test set and support set }{\pageref{classpgpr__data}}{}
\item\contentsline{section}{\hyperlink{classpgpr__domain}{pgpr\+\_\+domain} \\*This class stores the domain data in a matrix manner }{\pageref{classpgpr__domain}}{}
\item\contentsline{section}{\hyperlink{classpgpr__fgp}{pgpr\+\_\+fgp} \\*This class provides basic regression function using full G\+P }{\pageref{classpgpr__fgp}}{}
\item\contentsline{section}{\hyperlink{classpgpr__matrix}{pgpr\+\_\+matrix$<$ T $>$} \\*Matrix class }{\pageref{classpgpr__matrix}}{}
\item\contentsline{section}{\hyperlink{classpgpr__parallel}{pgpr\+\_\+parallel} \\*Class provides the M\+P\+I\+C\+H interface to commmunicate among the machines }{\pageref{classpgpr__parallel}}{}
\item\contentsline{section}{\hyperlink{classpgpr__parse}{pgpr\+\_\+parse} \\*This class parses domain data files, configuration file and commandline of different applications }{\pageref{classpgpr__parse}}{}
\item\contentsline{section}{\hyperlink{classpgpr__pic}{pgpr\+\_\+pic} \\*This class provides the regression function using P\+I\+C Approximation }{\pageref{classpgpr__pic}}{}
\item\contentsline{section}{\hyperlink{classpgpr__pitc}{pgpr\+\_\+pitc} \\*This class provides the regression function using P\+I\+T\+C Approximation }{\pageref{classpgpr__pitc}}{}
\item\contentsline{section}{\hyperlink{classpgpr__plma}{pgpr\+\_\+plma} \\*This class provides the regression function using P\+L\+M\+A Approximation }{\pageref{classpgpr__plma}}{}
\item\contentsline{section}{\hyperlink{classpgpr__ppic}{pgpr\+\_\+ppic} \\*This class provides the regression function using P\+I\+C Approximation, but implemented in a paralle manner }{\pageref{classpgpr__ppic}}{}
\item\contentsline{section}{\hyperlink{classpgpr__ppitc}{pgpr\+\_\+ppitc} \\*This class provides the regression function using P\+I\+T\+C Approximation,implemented in a parallel manner }{\pageref{classpgpr__ppitc}}{}
\item\contentsline{section}{\hyperlink{classpgpr__timer}{pgpr\+\_\+timer} \\*This timer class can provide real-\/time measure (in seconds) incurred by a block of running program }{\pageref{classpgpr__timer}}{}
\item\contentsline{section}{\hyperlink{classpgpr__vector}{pgpr\+\_\+vector$<$ T $>$} \\*Vector class }{\pageref{classpgpr__vector}}{}
\item\contentsline{section}{\hyperlink{structt__command__demo}{t\+\_\+command\+\_\+demo} \\*Information parsed from commandline of application that demonstrates the regression algorithms }{\pageref{structt__command__demo}}{}
\item\contentsline{section}{\hyperlink{structt__command__prep}{t\+\_\+command\+\_\+prep} \\*Information parsed from commandline of application that prepares the experimental data }{\pageref{structt__command__prep}}{}
\item\contentsline{section}{\hyperlink{structt__global__summary}{t\+\_\+global\+\_\+summary} \\*This structure is used for global\+\_\+summary }{\pageref{structt__global__summary}}{}
\item\contentsline{section}{\hyperlink{structt__local__summary}{t\+\_\+local\+\_\+summary} \\*This structure is used for local\+\_\+summary }{\pageref{structt__local__summary}}{}
\item\contentsline{section}{\hyperlink{structt__state}{t\+\_\+state} \\*Status (e.\+g., modified or not) of a set of elements }{\pageref{structt__state}}{}
\end{DoxyCompactList}
